{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np  # 矩阵操作\n",
    "import pandas as pd # SQL数据处理\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "import matplotlib.pyplot as plt   #画图\n",
    "import seaborn as sns\n",
    "\n",
    "# 图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = 'G:/'\n",
    "data = pd.read_csv(dpath+\"day.csv\")\n",
    "#通过观察前5行，了解数据每列（特征）的概况\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SXjUgmkCU8vBxSTXJcZFBMevgQHz7nIlUNbbx/PoKp0NRQUwTiFK2w3UtlFY1MT8/NeQ6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\npQKiCUQppVRA/h+44clKY5EX0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc22d080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('Median value of owner-occupied homes', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 15)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SjXMLcWlSDe+Dv9Dm0AwqDvTJsFxx7zp0iVqNY7VSANgUyk+zDSPoELaYWhN7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\n/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1697f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('house_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "data11 = data[data.yr==0]\n",
    "data12 = data[data.yr==1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "data12.shape\n",
    "y_train = data11['cnt'].values\n",
    "X_train = data11.drop(['casual','registered','cnt','dteday'], axis = 1)\n",
    "\n",
    "y_test = data12['cnt'].values\n",
    "X_test = data12.drop(['casual','registered','cnt','dteday'], axis = 1)\n",
    "\n",
    "#用于后续显示权重系数对应的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data.drop(['casual','registered','cnt','dteday'], axis = 1)\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理／特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = preprocessing.StandardScaler()\n",
    "ss_y = preprocessing.StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "#X_train = preprocessing.scale(X_train)\n",
    "#X_test =   preprocessing.scale(X_test)\n",
    "\n",
    "X_train = preprocessing.MinMaxScaler().fit_transform(X_train)\n",
    "X_test =   preprocessing.MinMaxScaler().fit_transform(X_test)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 确定模型类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[2.90778130056]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1.89183102744]</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.79225398552]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.105257322126]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[0.0163214798311]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-6.66133814775e-15]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.217015931007]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.283748910607]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-0.349392811772]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-0.772399498703]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-0.809657189297]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-2.03759143164]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    coef     columns\n",
       "8        [2.90778130056]        temp\n",
       "3        [1.89183102744]        mnth\n",
       "1        [0.79225398552]      season\n",
       "5       [0.105257322126]     weekday\n",
       "6      [0.0163214798311]  workingday\n",
       "2   [-6.66133814775e-15]          yr\n",
       "4      [-0.217015931007]     holiday\n",
       "9      [-0.283748910607]       atemp\n",
       "10     [-0.349392811772]         hum\n",
       "11     [-0.772399498703]   windspeed\n",
       "7      [-0.809657189297]  weathersit\n",
       "0       [-2.03759143164]     instant"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.675581129972\n",
      "The r2 score of LinearRegression on train is 0.760914194759\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print 'The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr)\n",
    "#训练集\n",
    "print 'The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXK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\nzFwE/H4Hs8wGrs6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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd56ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 正则化的线性回归（L2正则 --> 岭回归）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.67516780778\n",
      "The r2 score of RidgeCV on train is 0.757881048863\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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446omM5sN/JRYUOyNmz/azAYHr8cC1wHxJ8ZFRCSFIjsM5e7tZrYUeB0oBJ50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\nkpDCQkREElJYiIhIQgoLERFJ6P8DCIrbaEIgulAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd56ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 1.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[2.90778130056]</td>\n",
       "      <td>[1.46746622301]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1.89183102744]</td>\n",
       "      <td>[0.204609452944]</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.79225398552]</td>\n",
       "      <td>[0.783895703748]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.105257322126]</td>\n",
       "      <td>[0.10358737985]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[0.0163214798311]</td>\n",
       "      <td>[0.0190398892589]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-6.66133814775e-15]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.217015931007]</td>\n",
       "      <td>[-0.18727452049]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.283748910607]</td>\n",
       "      <td>[1.21138277926]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-0.349392811772]</td>\n",
       "      <td>[-0.329473687911]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-0.772399498703]</td>\n",
       "      <td>[-0.667162829924]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-0.809657189297]</td>\n",
       "      <td>[-0.788655886446]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-2.03759143164]</td>\n",
       "      <td>[-0.191702271619]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 coef_lr         coef_ridge     columns\n",
       "8        [2.90778130056]    [1.46746622301]        temp\n",
       "3        [1.89183102744]   [0.204609452944]        mnth\n",
       "1        [0.79225398552]   [0.783895703748]      season\n",
       "5       [0.105257322126]    [0.10358737985]     weekday\n",
       "6      [0.0163214798311]  [0.0190398892589]  workingday\n",
       "2   [-6.66133814775e-15]              [0.0]          yr\n",
       "4      [-0.217015931007]   [-0.18727452049]     holiday\n",
       "9      [-0.283748910607]    [1.21138277926]       atemp\n",
       "10     [-0.349392811772]  [-0.329473687911]         hum\n",
       "11     [-0.772399498703]  [-0.667162829924]   windspeed\n",
       "7      [-0.809657189297]  [-0.788655886446]  weathersit\n",
       "0       [-2.03759143164]  [-0.191702271619]     instant"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 正则化的线性回归（L1正则 --> Lasso）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.653598175673\n",
      "The r2 score of LassoCV on train is 0.748113970313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ai\\Anaconda2\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "lasso = LassoCV(alphas=alphas)   \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xfc180f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.01)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.481851</td>\n",
       "      <td>[2.90778130056]</td>\n",
       "      <td>[1.46746622301]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[1.89183102744]</td>\n",
       "      <td>[0.204609452944]</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.780160</td>\n",
       "      <td>[0.79225398552]</td>\n",
       "      <td>[0.783895703748]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.019892</td>\n",
       "      <td>[0.105257322126]</td>\n",
       "      <td>[0.10358737985]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.0163214798311]</td>\n",
       "      <td>[0.0190398892589]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-6.66133814775e-15]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.217015931007]</td>\n",
       "      <td>[-0.18727452049]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-0.283748910607]</td>\n",
       "      <td>[1.21138277926]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.349392811772]</td>\n",
       "      <td>[-0.329473687911]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.327067</td>\n",
       "      <td>[-0.772399498703]</td>\n",
       "      <td>[-0.667162829924]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.819279</td>\n",
       "      <td>[-0.809657189297]</td>\n",
       "      <td>[-0.788655886446]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-2.03759143164]</td>\n",
       "      <td>[-0.191702271619]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coef_lasso               coef_lr         coef_ridge     columns\n",
       "8     2.481851       [2.90778130056]    [1.46746622301]        temp\n",
       "3     0.000000       [1.89183102744]   [0.204609452944]        mnth\n",
       "1     0.780160       [0.79225398552]   [0.783895703748]      season\n",
       "5     0.019892      [0.105257322126]    [0.10358737985]     weekday\n",
       "6     0.000000     [0.0163214798311]  [0.0190398892589]  workingday\n",
       "2     0.000000  [-6.66133814775e-15]              [0.0]          yr\n",
       "4    -0.000000     [-0.217015931007]   [-0.18727452049]     holiday\n",
       "9     0.000000     [-0.283748910607]    [1.21138277926]       atemp\n",
       "10   -0.000000     [-0.349392811772]  [-0.329473687911]         hum\n",
       "11   -0.327067     [-0.772399498703]  [-0.667162829924]   windspeed\n",
       "7    -0.819279     [-0.809657189297]  [-0.788655886446]  weathersit\n",
       "0     0.000000      [-2.03759143164]  [-0.191702271619]     instant"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 继续扩大超参数搜索范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.653598175673\n",
      "The r2 score of LassoCV on train is 0.748113970313\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "alphas = [0.00001, 0.0001, 0.001, 0.01]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "lasso = LassoCV(alphas=alphas)   \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NxCXRFCQikvbMjJumjmbjrgP89i/r299A4kpBIiJdwoSyIsaW9OPeBSs4eEijkmRSkIhI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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd591f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.01)\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
